# [R] distribution of the product of two correlated normal

Peter Ruckdeschel Peter.Ruckdeschel at uni-bayreuth.de
Mon Apr 24 11:36:48 CEST 2006

Yu, Xuesong writes:
>
> Does anyone know what the distribution for the product of two correlated
> normal? Say I have X~N(a, \sigma1^2) and Y~N(b, \sigma2^2), and the
> \rou(X,Y) is not equal to 0, I want to know the pdf or cdf of XY. Thanks
> a lot in advance.
>

There is no closed-form expression (at least not to my knowledge) ---
but you could easily write some code for a numerical evaluation of the pdf / cdf:

###############################################################################
#code by P. Ruckdeschel, peter.ruckdeschel at uni-bayreuth.de 04-24-06
###############################################################################
#
#pdf of X1X2, X1~N(m1,s1^2), X2~N(m2,s2^2), corr(X1,X2)=rho, evaluated at t
#
#   eps is a very small number to catch errors in division by 0
###############################################################################
#
dnnorm <- function(t, m1, m2, s1, s2, rho,  eps = .Machine$double.eps ^ 0.5){ a <- s1*sqrt(1-rho^2) b <- s1*rho c <- s2 f <- function(u, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c, eps = eps) { nen0 <- m2+c0*u #catch a division by 0 nen <- ifelse(abs(nen0)>eps, nen0, ifelse(nen0>0, nen0+eps, nen0-eps)) dnorm(u)/a0/nen * dnorm( t/a0/nen -(m1+b0*u)/a0) } -integrate(f, -Inf, -m2/c, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c)$value+
integrate(f, -m2/c,  Inf, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c)$value } ############################################################################### # #cdf of X1X2, X1~N(m1,s1^2), X2~N(m2,s2^2), corr(X1,X2)=rho, evaluated at t # # eps is a very small number to catch errors in division by 0 ############################################################################### # pnnorm <- function(t, m1, m2, s1, s2, rho, eps = .Machine$double.eps ^ 0.5){
a <- s1*sqrt(1-rho^2)
b <- s1*rho
c <- s2
fp <- function(u, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c,  eps = eps)
{nen0 <- m2+c0*u ## for all u's used in integrate: never negative
#catch a division by 0
nen  <- ifelse(nen0>eps, nen0, nen0+eps)
dnorm(u) * pnorm( t/a0/nen- (m1+b0*u)/a0)
}
fm <- function(u, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c,  eps = eps)
{
nen0 <- m2+c0*u ## for all u's used in integrate: never positive
#catch a division by 0
nen  <- ifelse(nen0< -eps, nen0, nen0-eps)
dnorm(u) * pnorm(-t/a0/nen+ (m1+b0*u)/a0)
}
integrate(fm, -Inf, -m2/c, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c)$value+ integrate(fp, -m2/c, Inf, t = t, m1 = m1, m2 = m2, a0 = a, b0 = b, c0 = c)$value
}
##############################################################################

If you have to evalute dnnorm() or pnnorm() at a lot of values of t
for some given m1, m2, s1, s2, rho, then you should first evaluate
[p,d]nnorm() on a (smaller) number of gridpoints of values for t first
and then use something like approxfun() or splinefun() to give you a
much faster evaluable function.

Hth, Peter



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